1.

Introduction

Mangrove ecosystems support various services,^1–3 including nutrient cycling, and serve as a vehicle for fisheries production that dominates the areas between tides along tropical⁴ and subtropical coasts.^5–7 Despite mangroves making only up 0.7% of tropical forests worldwide,^8,9 they contribute $\sim 50\%$ of carbon stocks.^10,11 Human activities have reduced the area of mangrove forests by 30% to 50% in the last half century due to excessive exploitation/logging,^2,11,12 conversion into pond areas, and the development of the areas.^2,13

Lampung is a coastal area covered by various mangroves¹⁴ including Rhizophora mucronata, which grows on muddy soil types and, occasionally, on sandy reefs.¹⁵ R. mucronata is distributed in the East Coast of Lampung, mainly in Pasir Sakti, East Lampung Regency. Sonneratia alba is another mangrove that grows in sandy mud. The lower leaves of Avicennia lanata and A. marina exhibit a similar morphology while being salty, exhibiting elliptical leaf tips,¹⁶ and being distributed in sandy areas with fine mud near estuaries in Ketapang subdistrict, South Lampung.¹⁷

Given the several types of scattered species that reflect the development and condition of mangroves, it is necessary to identify and classify mangroves. Several methods have been used previously for identification of mangrove by only in situ measurements^18,19 or by utilizing satellite imagery.^20,21 In situ mangrove monitoring provides the most accurate information on mangrove distribution; however, data collection through field surveys remains challenging due to limited accessibility to mangroves.²² Mangroves are located in relatively wet areas and subjected to high tides.² Thus remote sensing has become a practical way to map and monitor mangrove forests. Remote sensing provides land cover information using pixel-based analysis.²³

Furthermore, integration of artificial intelligence, mathematical algorithms, and big data analysis with high-resolution sensing imaging data has become more common.^24,25 These data can be collected on a regular basis over a wide geographic area, enabling precise, and accurate monitoring of mangrove forest ecosystems.²² The use of remote sensing technologies is expanding, along with the demand for spatial data. Remote sensing data are essential for extracting parameters and biophysical data in identifying mangrove forests.²⁶ These data in coastal areas can be utilized to monitor mangroves. Passive (optical) and active system synthetic aperture radar images are the two forms of remote sensing images that can be used for land monitoring.²⁶

The classification and segmentation of coastal objects, including mangrove cover and tidal marsh, allows for determining the extent of each object. Using machine learning techniques, including support vector machines (SVM),^5,22,25–28 support vector regression, artificial neural network,²⁹ random forest (RF),^22,30,31 decision tree, symbolic regression,³² extreme gradient boosting regression,^33,34 light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), allows for the retrieval of data on mangrove distribution. RF has an excellent biomass modeling ability³⁵ and can increase the precision of land cover mapping, wetland mapping,³⁶ and mangrove species classification.^5,34,37

Mangrove identification is performed based on mangrove canopy properties. This can be analyzed using vegetation index transformation.^24,38 One technique for changing vegetation indices is the green normalized difference vegetation index (GNDVI).¹⁷ This technique shows parameters related to vegetation,^28,39 such as green foliage biomass and area, which are essential for vegetation division. In addition, since mangroves are located in relatively wet areas, a moisture index, such as the normalized difference moisture index (NDMI)¹⁷ and a wetness index, such as the normalized difference water index (NDWI) can be used to accurately identify them.

Earlier studies have not differentiated mangrove species and focused only on mangrove forest size and distribution.⁴⁰ Mangrove species composition and distribution data are critical to understanding mangrove ecosystem functions and ensuring sustainable mangrove conservation.²⁹ However, mangroves of a single species typically form tiny patches or thin strips that are invisible on satellite images. Furthermore, using machine learning models and remote sensing data for mangrove species mapping is challenging since there is no clear zoning between species due to the spectral similarity of mangroves.²⁸ This underlies the importance of mangrove mapping at the species level. Correctly identifying species will provide insights into the relationships between them. In addition, mangrove species classification allows the monitoring of a particular species population over time. This will help in detecting changes in the population size, distribution, and health of mangrove species. Previous studies have used machine learning models for mangrove species mapping using SVM classification with Worldview-2 images and aerial photographs.^28,41 Behera et al.³⁸ identified the mangrove species Heritiera fomes, Excoecaria agallocha, and Avicennia officinalis using reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1. Meanwhile, Paramanik et al.⁴² only used AVIRIS-NG hyperspectral imagery, which successfully identified the species H. fomes, E. agallocha, and A. officinalis and two of their combinations (H. fomes–E. agallocha and A. officinalis–E. agallocha), and Ghorbanian et al.²² were limited to mapping mangrove ecosystems using Sentinel-1 and Sentinel-2 images using RF. For developing the classification model, the parameter used was vegetation index;³⁸ this is in contrast to the present study, which not only used the vegetation index but also the wetness and moisture indices. In addition, the directly measured reflectance value was also a parameter used for further modeling, which improved the accuracy of classification mapping.

Mangrove species can be categorized by changing the algorithm or applying new information.²⁸ Mapping and classifying mangrove species can be accomplished using a combination of RF algorithms and field spectroradiometers. Herein, the leaf area index,⁴³ vegetation texture and index,⁴⁴ humidity, and wetness were used as parameters. High-resolution data are useful for classifying mangrove species; nonetheless, they are not available for all areas.⁴⁵

Previous studies have investigated mangrove reflectance using satellite data; however, observations of the spectral properties of mangrove biota using satellite images are insufficient, owing to the challenge of recognizing mangrove species solely from the canopy.⁴⁶ This is supported by previous research showing that object reflectance using a field spectroradiometer can be directly measured for mangrove species mapping.⁴⁵ Other findings have indicated that each mangrove species has a unique spectral reflectance and can be easily identified and mapped with adjacent wavelength bands in the near-infrared (NIR) region.¹⁵

Information on mangrove species is essential for mangrove management; however, insufficient research related to species classification has been conducted, especially in Lampung, Indonesia. Therefore, the objective of this research is to investigate mangrove species mapping using integrated Sentinel-2 imagery and field spectral data processed by an RF algorithm. The novelty of this study lies in the determination of the best parameters for RF classification, thus enhancing the accuracy of identifying mangrove species for the management, monitoring, and rehabilitation of mangroves in study areas.

2.

Materials and Methods

2.1.

Study Site

The study was chosen along the coastlines of Lampung, Indonesia, specifically targeting the east⁴⁷ and south areas and located between 5°25′30″S–5°51′0″S and 105°30′0″E–105°52′0″E. Tidal swamp plains are found along the east coast with an elevation of $\sim 0.5$ to 1 m above the mean sea level, and sedimentation areas based on the rising tide characterize the research site in these two districts (East and South Lampung) (Fig. 1).

Fig. 1

(a) Study area (RGB Sentinel-2A: August 6–15, 2022), (b) sampling points in Pasir Sakti, and (c) sampling points in Ketapang.

2.3.

Methodological Approach

SNAP 9.0.0 version was used for Sentinel-2A image preprocessing, whereas Endmapbox was used for processing of field spectroradiometer data, and quantum GIS was used for RF classification. The spectral bands used were red, green, and NIR. The methodology included radiometric and geometric correction, vegetation index transformation, moisture, and water indices, direct spectral measurements in the field, classification, accuracy testing, and mangrove species mapping. Fig. 2 outlines the research process.

Fig. 2

Flowchart of the methodology applied in this study.

Accuracy testing with a confusion matrix method is required for RF algorithm classification.⁵¹ It is possible to determine whether the classification results are sourced from two separate classes by comparing the derivatives of each predicted class in the matrix to the derivative of the actual class in each row. For remote sensing picture classification, the most effective and practical validation tool is the confusion matrix method.⁵² The vast majority of operations are consolidated into the error matrix, which use producer accuracy (PA), user accuracy (UA), and overall accuracy (OA) as indicators; this method is therefore successful.⁵³ A test of the classification results’ correctness was performed to gauge the degree of precision of the usage map produced by the digital classification technique. Although the samples from the training area and accuracy test were different, the accuracy of the accuracy test sample was more commonly acknowledged because it was taken in a different area.⁵⁴

2.3.1.

Image preprocessing

The preprocessing analysis was divided into two main stages: radiometric and geometric corrections. In the radiometric correction analysis, the type of image used was Sentinel-2A MSI Multispectral Instrument, level 1C,⁵⁵ a product that has not been radiometrically and atmospherically corrected; therefore, pixel errors due to atmospheric influences must be minimized. The best image with $<10\%$ cloud cover was chosen for preprocessing. The selected recording period was between January 1 and December 31, 2022. The best data were the Sentinel-2A image recording on August 7, 2022, which had the minimum cloud cover such that the objects beneath were clearly visible. For each of the image’s multispectral channels, radiometric calibration was carried out by translating digital values (DN) into radians.

The radian image was converted to top of atmosphere reflectance after radiometric calibration. The main objective was to correct for differences in reflectance values due to variations in the Earth–Sun distance on each recording date.⁵⁶ These differences can be significant owing to the differences in geographical conditions and the time of image recording. The fast line-of-sight atmospheric analysis of spectral hypercubes atmospheric correction method was used for the correction to reduce atmospheric impacts.⁵⁶ Sentinel-2A image processing was used to facilitate the analysis of the mangrove cover during the geometric correction stage. The geometric correction used in this study was image-to-map, with the reference data being an Indonesian Landform map based on ground control points (GCPs) collected directly in the field. The interpolation method used was a nearest-neighbor algorithm that only retrieves the nearest pixel value shifted to a new position. Six GCPs were used in the geometric correction, with a root-mean-square error value of 0.33 pixel.

2.3.2.

Mangrove reflectance

The ASD HandHeld 2: hand-held visible NIR spectroradiometer (ASD Inc., Alpharetta, Georgia, United States) was used for mangrove reflectance measurements. The spectroradiometer was calibrated with a white reference prior to use⁴⁵ and recalibrated in cases where a significant difference in light intensity was noticed during use or when a “saturation alert” warning was issued.¹⁵ A total of 144 reflectance values were obtained, and measurements were performed between 09.00 and 14.00 WIB to reduce the influence of weather at the research site. The angular position of the spectroradiometer sensor was set at 45 deg to the direction of sunlight to avoid shadows on the target object.⁵⁷ Measurements were made on land and partly using boats owing to difficult access to the sites.

The presence of mangroves above the water was sufficient to affect the spectral value due to the possibility of water reflection interfering with the mangrove reflectance value. The supported data output format was .txt, with data collection conducted from August 6 to 15, 2022. The Sentinel-2A image recording date was August 7, 2022. Table 2 lists a comparison of the wavelengths used in the Sentinel-2A image and the spectroradiometer. The results of the in situ measurements were used to develop a spectrum library⁵⁸ to compare the spectral image to the reference spectrum. The spectrum library was used as the reference to compare the Sentinel-2A image reflectance value to the field reflectance value.⁵⁹ The pixel values of mangrove species were used as the target spectrum for spectral matching-based object classification.

Table 2

Wavelength bands used by Sentinel-2A and the spectroradiometer.

Sentinel-2A		Spectroradiometer
Wavelength (nm)	Band name	Wavelength (nm)	Band name
433 to 453	Coastal aerosol	315 to 400	Violet
458 to 523	Blue	400 to 525	Blue
543 to 578	Green	525 to 605	Green
650 to 680	Red	605 to 655